Citation: | ZHAO Chen, SUN Binbin, ZHOU Guohua, HE Ling, ZENG Daoming. 2023. Investigation on the characteristics nutrient content and prediction of available nutrients content in the soil of Longhai, Fujian Province. Geological Bulletin of China, 42(10): 1784-1791. doi: 10.12097/j.issn.1671-2552.2023.10.014 |
In this study, samples were collected in Longhai City, Fujian, under different geological backgrounds, land use types, soil types, and soil formation conditions.A total of 56 soil samples were collected and analyzed for pH value, organic matter content(SOM), cation exchange capacity(CEC), total and available nutrient content.Statistical methods such as correlation analysis and linear regression analysis were used to explore the factors influencing the available nutrient content in the soil.A prediction model for the available nutrient content in the soil of the study area was established and validated.This study aims to gain a comprehensive understanding of the nutrient status of the soil in the area and provide scientific basis for the rational utilization and management of land in the region.The results show that in the study area, the soil in Longhai City is characterized by acidity to strong acidity, low organic matter content, and a relatively low cation exchange capacity.The soil shows relative enrichment of elements such as N, P, K, Mo, and Zn in terms of total quantity.There is also significant variability in the total quantity, available quantity, and effectiveness of these elements.The soil analysis data reveals good correlations between the total and available quantities of nutrients such as B, Cu, K, and Mn.The pH of the soil has a certain influence on the effectiveness of K, Mo, and Si, while the cation exchange capacity(CEC) shows a significant positive correlation with the effectiveness of Si.Increasing soil organic matter(SOM) is beneficial for improving the available quantities of elements such as Cu, Fe, Mn, P, and Zn.Linear models based on soil analysis data and soil physicochemical indicators can be used to predict the available quantities of nutrients such as Cu, Fe, Mo, P, S, and Si in the surface soil.Introducing more influencing factors would further improve the fitting effect of the models and enhance the accuracy of predictions.
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Location of the study area and sampling sites